[图书][B] Dynamics and Bifurcation in Networks: Theory and Applications of Coupled Differential Equations
M Golubitsky, I Stewart - 2023 - SIAM
The biologist JBS Haldane, when asked what we can learn about the Creator by examining
the world, is said to have replied “an inordinate fondness for beetles”[705]. Today's …
the world, is said to have replied “an inordinate fondness for beetles”[705]. Today's …
Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution
We can now measure the connectivity of every neuron in a neural circuit, but we are still
blind to other biological details, including the dynamical characteristics of each neuron. The …
blind to other biological details, including the dynamical characteristics of each neuron. The …
From calcium imaging to graph topology
Abstract Systems neuroscience is facing an ever-growing mountain of data. Recent
advances in protein engineering and microscopy have together led to a paradigm shift in …
advances in protein engineering and microscopy have together led to a paradigm shift in …
Sequential attractors in combinatorial threshold-linear networks
Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and
central pattern generator circuits that underlie rhythmic behaviors like locomotion. While …
central pattern generator circuits that underlie rhythmic behaviors like locomotion. While …
Core motifs predict dynamic attractors in combinatorial threshold-linear networks
C Parmelee, S Moore, K Morrison, C Curto - PloS one, 2022 - journals.plos.org
Combinatorial threshold-linear networks (CTLNs) are a special class of inhibition-dominated
TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of …
TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of …
Fixed points of competitive threshold-linear networks
Threshold-linear networks (TLNs) are models of neural networks that consist of simple,
perceptron-like neurons and exhibit nonlinear dynamics determined by the network's …
perceptron-like neurons and exhibit nonlinear dynamics determined by the network's …
Diversity of emergent dynamics in competitive threshold-linear networks
Threshold-linear networks consist of simple units interacting in the presence of a threshold
nonlinearity. Competitive threshold-linear networks have long been known to exhibit …
nonlinearity. Competitive threshold-linear networks have long been known to exhibit …
Pattern completion in symmetric threshold-linear networks
C Curto, K Morrison - Neural computation, 2016 - ieeexplore.ieee.org
Threshold-linear networks are a common class of firing rate models that describe recurrent
interactions among neurons. Unlike their linear counterparts, these networks generically …
interactions among neurons. Unlike their linear counterparts, these networks generically …
Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
R Pang, AL Fairhall - Elife, 2019 - elifesciences.org
Cognitive flexibility likely depends on modulation of the dynamics underlying how biological
neural networks process information. While dynamics can be reshaped by gradually …
neural networks process information. While dynamics can be reshaped by gradually …
Periodic solutions in threshold-linear networks and their entrainment
Threshold-linear networks (TLNs) are recurrent networks where the dynamics are threshold-
linear (linearly rectified at zero). Mathematically, they consist of coupled nonsmooth ordinary …
linear (linearly rectified at zero). Mathematically, they consist of coupled nonsmooth ordinary …